Machine learning (ML) has emerged as a popular tool for performing analysis of image datasets generated from various microscopy techniques [1]}, [2]}. The initial thrust of ML in this field focused on demonstrating the utility of ML to fulfil analysis objectives such as microstructure classification [3]}, structural feature segmentation [4]}, creation of artificial microstructure images to augment training datasets [5]}, etc. A recently emerged focus area is the need to carry forward this body of knowledge and build ML applications that can analyze experimental data as it is produced and with reduced human intervention. An autonomous system presents multiple challenges, especially in the context of electron microscopy datasets. One of them is the limited number of images and labels available for training a ML model. The appropriate training dataset size scales with the number of model parameters, and hence it is generally difficult to train a deep neural network from scratch using only images from the microscopy datasets. A common approach to overcome this challenge is to implement transfer learning [6]}. This was followed in the current work. A second challenge in this field is that the image data distribution is dependent on factors such as experimental conditions (temperature, ambient atmosphere, etc.), sample quality, and imaging conditions (focus, tilt, etc.). This makes it possible for shifts to occur from the data distribution in one experiment to the distribution in the next. Keeping these challenges in mind, a few overarching goals were identified for the ML model to achieve. First, the model should continue to provide good performance on new data. Second, the model should be calibrated well within the domain of the data distribution, following which the model should be able to estimate the uncertainty associated with its output. This will aid in the identification of data with excessive noise and data with a shift in distribution from that of the training data.
Common uncertainty quantification (UQ) approaches incorporated in ML models include the use of Bayesian optimization for parameter estimation, Monte-Carlo dropout, and ensemble learning. A comprehensive review of such techniques can be found in [7]} and [8]}. An overview of UQ in a related field to microscopy analysis, biomedical image segmentation, can be found in [9]}.
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